Diffusion inversion, which maps images back to the Gaussian latent space of a diffusion model, is a critical task for image reconstruction and editing. While DDIM enables fast deterministic inversion, it inherently introduces deviations that accumulate into noticeable inversion errors. Existing methods often address this by solving a fixed-point problem but largely overlook how the selection of the diffusion timestep in the noise scheduler influences inversion fidelity. In this work, we reveal that the deviation scale in diffusion inversion is strongly dependent on the timestep size, and exhibits a parabolic trend, with larger errors concentrated at both small and large timesteps. Based on this finding, we propose a simple yet effective nonuniform timestep scheduler that integrates a global rescaling with a local dynamic programming based rescheduling, enabling a strategic allocation of computational effort that minimizes the overall inversion error and preserves higher inversion accuracy. Our method serves as an off-the-shelf enhancement for existing inversion techniques and requires no extra parameters or computational overhead. Through extensive experiments, we verify that integrating our scheduler consistently boosts the performance of existing inversion methods, achieving superior results in image reconstruction and editing.

翻译：扩散逆映射将图像映射回扩散模型的高斯潜空间，是图像重建与编辑中的关键任务。尽管DDIM支持快速确定性逆映射，但其固有偏差会累积形成显著的逆映射误差。现有方法通常通过求解不动点问题来应对这一挑战，却普遍忽视了噪声调度器中扩散时间步长的选择对逆映射保真度的影响。本研究揭示了扩散逆映射中的偏差尺度强烈依赖于时间步长大小，并呈现抛物线趋势——较大误差集中在极小和极大时间步长处。基于此发现，我们提出一种简洁高效的非均匀时间步长调度器，该调度器融合全局重缩放与基于局部动态规划的重调度机制，通过策略性分配计算资源来最小化整体逆映射误差并保持更高逆映射精度。本方法可作为现有逆映射技术的即用型增强方案，无需额外参数或计算开销。大量实验证实，集成该调度器后，现有逆映射方法性能持续提升，在图像重建与编辑任务中均取得更优效果。